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Data Mining vs Artificial Intelligence

Artificial intelligence (AI) and data mining are fascinating concepts with so much gravity in today's industry to improve both work and personal lives. First, let's start by looking at the difference between data mining and Artificial Intelligence. Although there is a strong correlation and considerable overlap between the two, they are distinct and have different applications.

What is Data Mining?

Data mining aims to discover previously unseen patterns and relationships from large datasets and derive a business value. It focuses on uncovering relationships between two or more variables in your dataset and extracting insights. These insights include mapping the data into information directly relevant to a particular use case, such as predicting outcomes from incoming events and prescribing actions.

Data is reviewed for patterns, and then criteria are applied to determine the most frequent and important relationships. Multiple data sorting techniques can be used to accomplish this goal, such as clustering, classification, and sequence analysis. Data mining typically uses batched information to reveal a new insight at a particular time rather than an ongoing basis. For example, it can be used to identify a sales trend or buying pattern, improve a production process and predict the adoption of a new product.

Applications of Data Mining

Data mining is applied to multiple fields. Machine Learning enables businesses to grasp more about their customers, develop more productive strategies related to various business functions, and use resources acceptably and intuitively.

Benefits of Data Mining

Data Mining assists businesses in fulfilling their objectives and making the right decisions. Data mining involves powerful data collection and warehousing as well as computer processing. Data mining uses complex mathematical algorithms to segregate the data and gauge the probability of future events.

Key features of Data Mining

Below are some key features of data mining, such as:

  • Predictions are gathered automatically based on trend and behavior analysis.
  • Predictions are formulated based on likely outcomes.
  • Decision-oriented information.
  • Centers around large data sets and databases for analysis.

What is Artificial Intelligence?

Artificial intelligence or AI is simply an algorithm, code, or technique that enables machines to mimic, develop, and demonstrate human cognition or behavior. AI is a real-life data product capable of carrying out set tasks and solving problems roughly the same as humans do in the business world. The functions of AI systems encompass learning, planning, reasoning, decision-making, and problem-solving.

AI is majorly used for finding expert systems, natural language processing, speech recognition, and machine vision. AI is supreme in providing insights to enterprises on their operations. Particularly when it comes to monotonous, meticulous tasks like inspecting large numbers of legal documents to guarantee relevant fields are filled in properly. AI tools often complete jobs quickly and with relatively few errors. AI has incited an explosion of efficiency and unfolded the door to entirely new business opportunities for some larger enterprises.

Types of Artificial Intelligence

There are two types of artificial intelligence:

  1. Weak AI: Narrow AI or Artificial Narrow Intelligence (ANI). It is AI trained and focused on performing specific tasks. Weak AI drives most of the AI that surrounds us today. 'Narrow' might be a more accurate descriptor for this type of AI as it is anything but weak; it enables some very robust applications, such as Apple's Siri, Amazon's Alexa, IBM Watson, and autonomous vehicles.
  2. Strong AI: It is made up of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). Artificial general intelligence (AGI), or general AI, is a theoretical form of AI where a machine would have an intelligence equal to humans. It would have a self-aware consciousness that can solve problems, learn, and plan for the future.

Artificial Super Intelligence (ASI) would surpass the intelligence and ability of the human brain. While strong AI is still entirely theoretical with no practical examples in use today, that doesn't mean AI researchers aren't also exploring its development.

Artificial intelligence use cases

While there are at least several decades separating us from the human-like robot level of AI, scientists are already doing tons of mind-blowing things with narrow AI, for example, chatbots. Due to understanding speech and text in natural language, AI systems communicate with humans natural, personalized way. Other examples of AI are self-driving cars, robots used in manufacturing, and email spam filters, to name a few.

Difference between Artificial Intelligence and Data Mining

Artificial Intelligence is the study of creating intelligent machines which can work like humans. It does not depend on learning or feedback. Rather it has directly programmed control systems. AI systems come up with solutions to problems on their own by calculations.

The AI systems use the data mining technique in mined data to create solutions. Data mining serves as a foundation for artificial intelligence. Data mining is a part of programming codes with information and data necessary for AI systems.

Artificial Intelligence and Data Mining are closely interrelated and inevitable in the present-day world. These technologies will extremely succeed in the future with further advancements in technology. They will automate the manual process, build up sales and profits and uplift the businesses. Below are the following differences between artificial intelligence and data mining, such as:

Terms Artificial Intelligence Data Mining
Concept AI aims to facilitate software that can reason on input and explain output. AI provides a human-like interaction with software and offers decision support for specific tasks, but it's not a replacement for humans. It finds insights and draws predictions for the future.
  • Potential to deal with large datasets
  • Higher speed
  • Make innovations, design, and develop higher-yielding products and services.
  • Aids in discovering how different attributes of data sets are correlated through patterns and data visualization techniques.
Working method
  • AI works by integrating huge amounts of data with speedy, iterative processing and intelligent algorithms.
  • Digs deep into data and draws useful information from it.
  • Manufacturing robots
  • Self-driving cars
  • Smart assistants
  • Proactive healthcare management
  • Disease mapping
  • Automated financial investing
  • Virtual travel booking agent
  • Social media monitoring
  • Inter-team chat tool
  • Conversational marketing bot
  • Natural Language Processing (NLP) tools
  • Web mining, text mining, fraud detection
Human intervention AI-based machines are quick, precise, and logical, but they aren't inherent. They lack emotions and are not culturally sensitive. It required- Manual technique.
  • Scikit Learn
  • TensorFlow
  • Theano
  • Caffe
  • MxNet
  • Keras
  • PyTorch
  • CNT
  • Rapid Miner
  • Oracle Data Mining
  • IBM SPSS Modeler
  • Knime
  • Python
  • Orange
  • Kaggle
  • Rattle
  • Artificial general intelligence
  • Planning
  • Computer vision
  • General game playing
  • Knowledge reasoning
  • Machine learning
  • Natural language processing
  • Robotics
  • Future Healthcare
  • Market Basket Analysis
  • Manufacturing Engineering
  • CRM
  • Fraud Detection
  • Intrusion Detection
  • Customer Segmentation
  • Financial Banking

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